Multidisciplinary adolescent and young adult neuro-oncology clinic: Clinical cases, practice challenges, and future perspectives
Bibliographic record
Abstract
Background: The distinct tumor histopathology, molecular features, and psychosocial needs among adolescents and young adults (AYA) with brain tumors pose challenges within traditional healthcare systems. Establishing a multidisciplinary AYA neuro-oncology clinic has been proposed to address these gaps in care. This is the first study to describe the framework and patient profile of a multidisciplinary AYA neuro-oncology clinic in a quaternary cancer center in Canada. Methods: Clinic framework was outlined and patients seen from December 2022 to June 2024 were included. Demographic profiles, tumor characteristics, treatment details, clinical trial enrollment, and allied health referrals were collected. Barriers encountered were summarized. Results: The clinic is composed of specialists in pediatric and adult neuro-oncology with seamless referrals to neurosurgery, radiation oncology, and allied health teams. A total of 100 patients (males 54%, females 46%) were seen with a median age of 24 years. Pediatric-type low-grade glioma (PLGG) was the leading diagnosis. BRAF alterations were the primary molecular drivers. Twenty-nine patients received active neuro-oncology management in the clinic. Overall, 77 patients underwent at least one surgery, 31 patients received radiotherapy, and 43 patients received chemotherapy. Trametinib was the primary targeted treatment prescribed. Three patients were eligible and enrolled in clinical trials. Barriers identified included a lack of peer support groups and a paucity of available clinical trials. Conclusions: This study provides insight into the clinical profile of patients seen in a multidisciplinary AYA neuro-oncology clinic in Canada. Multidisciplinary care is feasible and integral in addressing the multifaceted needs of AYAs with brain tumors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".